A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete understanding of potentially druggable immune mediators of disease. To advance this, we present a comprehensive multi-omic blood atlas in patients with varying COVID-19 severity and compare with influenza, sepsis and healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity revealed cells, their inflammatory mediators and networks as potential therapeutic targets, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Tensor and matrix decomposition of the overall dataset revealed feature groupings linked with disease severity and specificity. Our systems-based integrative approach and blood atlas will inform future drug development, clinical trial design and personalised medicine approaches for COVID-19.

Type: Other
Archiver: European Genome-Phenome Archive (EGA)

13 Datasets 6 Publications

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001007931	Anonymised patient metadata and associated data dictionary. For further information regarding this dataset, please contact Alexander Mentzer at contact@combat.ox.ac.uk.		611
EGAD00001007932	SmartSeq2 RNAseq data from 16 samples. For further information regarding this dataset, please contact Julian Knight and Alexander Mentzer at contact@combat.ox.ac.uk.	NextSeq 500	16
EGAD00001007957	Bulk RNAseq data from whole blood. For further information regarding this dataset, please contact Katie Burnham and Andew Kwok at contact@combat.ox.ac.uk.	Illumina NovaSeq 6000	144
EGAD00001007959	gVCF file per patient obtained from the bulk/mini-bulk RNAseq data. For further information regarding this dataset, please contact Stephen Sansom and Alexander Mentzer at contact@combat.ox.ac.uk.		228
EGAD00001007960	fastq and filtered fasta files for B-cell receptor sequencing. For further information regarding this dataset, please contact Rachael Bashford-Rogers at contact@combat.ox.ac.uk.	Illumina MiSeq	96
EGAD00001007961	fastq and filtered fasta files for T-cell receptor sequencing. For further information regarding this dataset, please contact Rachael Bashford-Rogers at contact@combat.ox.ac.uk.	Illumina MiSeq	91
EGAD00001007962	Raw Illumina sequencing data and CellRanger BAM output files. For further information regarding this dataset, please contact Stephen Sansom at contact@combat.ox.ac.uk.	Illumina NovaSeq 6000	10
EGAD00001007963	Raw Illumina sequencing data from single-cell ATACSeq experiments. For further information regarding this dataset, please contact Julian Knight and Tatjana Sauka-Spengler at contact@combat.ox.ac.uk.	Illumina NovaSeq 6000	1
EGAD00001007964	Raw Illumina sequencing data. For further information regarding this dataset, please contact Rachael Bashford-Rogers at contact@combat.ox.ac.uk.	Illumina NovaSeq 6000	10
EGAD00001007965	Raw Illumina sequencing data. For further information regarding this dataset, please contact Benjamin Fairfax and Rachael Bashford-Rogers at contact@combat.ox.ac.uk.	Illumina NovaSeq 6000	10
EGAD00001008007	Raw Illumina sequencing data and CellRanger BAM output files. For further information regarding this dataset, please contact Stephen Sansom at contact@combat.ox.ac.uk.	Illumina NovaSeq 6000	10
EGAD00001008008	Linker file for COMBAT CITEseq sequencing data. Links COMBAT sample IDs with sequencing pools and their associated raw sequence data. Sequence data can be found in the following datasets: ADT data: EGAD00001007962 GEX data: EGAD00001008007 VDJ (B-cell): EGAD00001007964 VDJ (T-cell): EGAD00001007965		140
EGAD00001008009	Other raw and processed phenotype data generated by the COMBAT consortium.		611

Publications	Citations
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium. Electronic address: julian.knight@well.ox.ac.uk, COvid-19 Multi-omics Blood ATlas (COMBAT) Consortium. Cell 185: 2022 916-938.e58	154
COMBATdb: a database for the COVID-19 Multi-Omics Blood ATlas. Wang D, Kumar V, Burnham KL, Mentzer AJ, Marsden BD, Knight JC. Nucleic Acids Res 51: 2023 D896-D905	2
Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data. Cao Y, Ghazanfar S, Yang P, Yang J. Brief Bioinform 24: 2023 bbad159	2
Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2. Lin Y, Cao Y, Willie E, Patrick E, Yang JYH. Nat Commun 14: 2023 4272	7
Interferon-γ couples CD8<sup>+</sup> T cell avidity and differentiation during infection. Uhl LFK, Cai H, Oram SL, Mahale JN, MacLean AJ, Mazet JM, Piccirilli T, He AJ, Lau D, Elliott T, Gerard A. Nat Commun 14: 2023 6727	5
Interferon response and profiling of interferon response genes in peripheral blood of vaccine-naive COVID-19 patients. Huang B, Huang J, Chiang NH, Chen Z, Lui G, Ling L, Kwan MYW, Wong JSC, Mak PQ, Ling JWH, Lam ICS, Ng RWY, Wang X, Gao R, Hui DS, Ma SL, Chan PKS, Tang NLS. Front Immunol 14: 2023 1315602	0